The literature in reusing FrameNet for NLP tasks such as question-answering is
too large to be covered here, and not central to the work described (see e.g.
~\cite{ShenLapata07}~\cite{BurchardtPennacchiotti}).\newline Work in using
FrameNet jointly with other lexical resources, although not in the LOD way,
include at least ~\cite{Burchardtetal:05b}, which creates a linking from WordNet
to FrameNet in a purely NLP context. \newline Previous FrameNet conversions to
RDF include~\cite{Narayanan:2003:PFD:1119296.1119300, Scheffczyketal, Coppola2009Frame}. 
\cite{Narayanan:2003:PFD:1119296.1119300} proposes a partial translation of FrameNet
version 1.2 to RDF, and uses DAML both for the FrameNet meta-model, and the
conceptual elements (frames, elements, etc.). They developed an automatic
translator specific to that purpose. In 2003, the mixing of meta-model and
FrameNet data made it difficult to be processed by reasoners for OWL (but it'd
be acceptable in OWL2). For that reason, \cite{Scheffczyketal} applied an
ad-hoc XSLT to move part of the FrameNet version 1.3 XML database to OWL. While
the quality of the partial transformation is high, the process is not easily
customizable. ~\cite{Scheffczyketal} 
also proposes a solution to deductive reasoning with natural language 
based on combining lexical resources with the world knowledge provided by ontologies.
\newline After the release of version 1.5, the Berkeley FrameNet
group asked us to produce a new version of FrameNet in RDF, optimized for
publishing in the growing lexical part of the LOD cloud. This is what we
describe in this paper.\newline From the viewpoint of formal semantic
interpretation of FrameNet, ~\cite{Coppola2009Frame} uses both ABox and TBox
conversions to perform automatic enrichment of FrameNet with reference to a
large corpus where frames are detected, new frames and elements are discovered
and typed with a WordNet Supersense learner, and finally reengineered through a
previous alignment to the LMM semiotic ontology~\cite{LMM} (used in this paper
with the odp:semiotics.owl knowledge pattern.\newline ~\cite{gangemiOntolex} is
a deeper analysis of the semiotic relations behind FrameNet, VerbNet and
WordNet, and proposes a method to formalize their mappings. The semantics of the
frames is put in perspective with the Descriptions and Situations knowledge
pattern, partly reused in this paper to represent the situation-based semantics
declared by~\cite{fn2}. The article also proposes to represent the full semantics
of frames as n-ary polymorphic relations in FOL. This proposal is not directly
implementable in OWL, but provides a useful abstraction across the different
notions of a frame in cognitive science, AI, linguistics, knowledge engineering,
etc.\newline 
~\cite{OVCHINNIKOVA10.84} is an attempt to formalize and ``clean''
the semantics of FrameNet version 1.3. The authors motivate the cleansing need
by performing ``ontological analysis'': e.g. they claim that frames do not always
refer to situation classes because some of them actually represent abstract
relations such as \textit{part of}: since abstract entities should be assumed as
non-localized, non-temporal entities, while situations should be interpreted as
events occurring in time, frames should be formalized differently according to
their ontological type. Frame to frame relations are also suggested an extensive
revision on similar grounds. This work, besides the problem of sharing agreement on the general
principles adopted for the analysis,
%\footnote{In the past, similar cleaning efforts hit the limit of being ``extrinsic'' to the way lexicographers work.}, 
could benefit from a customized refactoring of FrameNet, in order to perform their analyses directly on formal 
ontologies.
%******** REVIEW ***************
% ISSUE 14
% Block added by Andrea
% Block rewritten by Aldo
% End of block
%The potential of frames has been demonstrated by~\cite{Coppola2009Frame}. In
%that work is presented an original approach that detects and learns frames
%and situations from textual domain corpora in order to populate ontologies with
%named entities and facts extracted from the same corpora. Frame detection
%presented in~\cite{Coppola2009Frame} learns typical patterns from the
%occurrences in a corpus, providing raw material to start a domain adaptation
%process of frames by means of a WordNet Supersense tagging component. The
%results are formalized by using the Linguistic Meta-Model~\cite{LMM}, that is
%used both for relating the results to other datasets (e.g. DBPedia) and to
%extract regular OWL TBoxes and ABoxes.\newline
%The OntoWordNet Project~\cite{gangemi:2003} was a research program aimed at
%achieving a formal specification of WordNet. Within this program, was developed
%a hybrid bottom-up top-down methodology to automatically extract association
%relations from WordNet, and to interpret those associations in terms of
%a set of conceptual relations, formally defined in the DOLCE~\cite{GangemiGMO03}
%foundational ontology.\newline
%There is a standard conversion of Princeton WordNet to
%RDF/OWL~\cite{Schreiber:06:RRW}, that is a W3C working draft since 19 June 2006. 
%The W3C WordNet schema has three main classes: Synset, WordSense and Word. The
%first two classes have subclasses for the lexical groups present in WordNet, e.g.
%NounSynset and VerbWordSense. This conversion aims at providing a
%standard conversion of WordNet for direct use by Semantic Web application
%developers and to improve interoperability of Semantic Web applications that use
%WordNet and simplify the choice between previous existing RDF/OWL versions, that
%have been used for the conversion on examining the commonalities and extending
%them where necessary and making choices to suit different needs of application
%developers.\newline
%knOWLer~\cite{Ciorascu2003} is an ontology-based information management system
%targeting semantic integration into large-scale information systems.
%The solution, proposed by knOWLer, for intergating Information Retrieval
%Systems into an ontology-based information management system, is obtained by
%using an OWL-ontology derived from the WordNetTM lexical database and a
%standard corpus obtained from the Wall Street Journal collection of TREC-4.
